Changes in the cerebral blood flow (CBF), measured using arterial spin labeling (ASL), are an emerging biomarker for normal aging, Alzheimer's disease, and other neurodegenerative conditions. However, ASL signal-to-noise ratio (SNR) is inherently low, diminishing the quality of CBF determination. While attempts have been made to improve SNR in ASL images using post-processing filters, performance is limited and several user-defined parameters are required adding further complexity in implementation. Here, we introduce a simple, novel filtering algorithm and demonstrate its potential to enhance the quality of CBF mapping.
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